Fundamentals of Python for Financial Econometrics

Chapter 34: Causal Machine Learning and Double Machine Learning

How can flexible prediction tools help estimate causal parameters without replacing identification assumptions?

Loading notebook...nuisance functionsorthogonal scoresresidualizationcross-fitting

Notebook lab guide

Related dataset

Ceteris Lab teaching sample

Estimated time

25 to 40 min

Packages

pandas, numpy, matplotlib

Expected output

Printed Python results that can be compared with the lesson explanation.

Learning goals

Load and inspect Ceteris Lab teaching sample.
Run the Python cells connected to Causal Machine Learning and Double Machine Learning.
Interpret the output using nuisance functions and orthogonal scores.

Common errors

File not found: check that wage_sample.csv is installed or use the course data folder.
Package import error: use the browser notebook first, then download for local Jupyter if your local packages differ.
Column name error: compare your variable names with the dataset variables listed for this notebook.

Dataset path helper

import pandas as pd

df = pd.read_csv("/data/wage_sample.csv")
df.head()

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